CaliPPer is a post‑hoc framework that calibrates and predicts the performance of binding‑prediction models by combining a multi‑chain Sample‑to‑Domain Distance (S2DD) metric with distance‑aware Bayesian recalibration. It operates at three resolutions—generalisability score, aggregate performance prediction, and per‑sample confidence—achieving strong distance‑performance correlations (|r| = 0.80–0.92) and low prediction errors for AUROC, AP, and F1. In retrospective analyses of five published studies, CaliPPer increased true discovery rates, improving AUROC by up to +0.20 on unseen epitopes and variants and raising confirmed neoantigen findings from 0/5 to 3/5.
arXiv:2607. 17601v1 Announce Type: cross Abstract: Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust.
Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust. Consensus scoring and ensemble methods improve mean accuracy but treat all predictions identically without interpretable confidence measures or uncertainty decomposition, ignoring the chemical context of each protein-ligand pair.
arXiv:2606. 28659v1 Announce Type: cross Abstract: High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be screened for vaccine design.
arXiv:2606. 04994v1 Announce Type: new Abstract: Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune engineering, yet existing models lack sufficient sensitivity and specificity for broad applications.
Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune engineering, yet existing models lack sufficient sensitivity and specificity for broad applications. A major limitation is the absence of rigorously defined, unseen benchmark datasets that allow unbiased evaluation of model performance and generalizability.
arXiv:2604.18467v3 Announce Type: replace-cross Abstract: Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterizat...
arXiv:2608.30175v1 Announce Type: new Abstract: Peptide-protein affinity models are often evaluated with a single data split, obscuring whether they interpolate among measurements for observed target...
arXiv:2603. 13431v3 Announce Type: replace-cross Abstract: Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized benchmark for fair comparison and model development.
ProbeMatchDTI introduces a probe-driven framework for drug‑target interaction prediction that preserves weak biochemical signals by using IterProbe to retain contextual states and BindingProbe to model cross‑entity complementarity at multiple scales. The method improves AUC‑ROC by 2.0% on BindingDB and 0.5% on DrugBank compared to prior biochemical representation learning approaches. Feature‑level analyses confirm the effectiveness of the probe-driven pattern matching, and the predictions are linked to an evidence‑guided downstream drug‑discovery workflow for candidate refinement and validation planning.
ProbeMatchDTI is a new framework for drug‑target interaction prediction that uses probe‑driven pattern matching to preserve weak biochemical signals. It introduces IterProbe, which retains contextual states across refinement depths and selects them with learnable probes, and BindingProbe, which models drug‑protein complementarity at both local and whole‑pair levels. Experiments show that ProbeMatchDTI outperforms existing methods, improving AUC‑ROC by 2.0% on BindingDB and 0.5% on DrugBank, and its predictions can be integrated into downstream drug‑discovery workflows.
arXiv:2602. 17330v5 Announce Type: replace-cross Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure clinically important minority clonotypes.
arXiv:2605. 21600v2 Announce Type: replace Abstract: Computational antibody CDR design methods condition on antigen structure to generate binding loops.